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Artigo de periodico
Integrating proximal geophysical sensing and machine learning for digital soil mapping: spatial prediction and model evaluation using a small dataset (2025)
Mello, Danilo César de; Veloso, Gustavo Vieira; Mello, Murilo Ferre de; Lana, Marcos Guedes de; Oliveira, Isabelle de Angeli ; Mello, Fellipe Alcantara de Oliveira ; Siqueira, Rafael Gomes; Gomes, Lucas Carvalho; Fernandes-Filho, Elpídio Inácio; Schaefer, Carlos Ernesto Gonçalves Reynaud; Francelino, Márcio Rocha; Leite, Emilson Pereira; Ferreira, Tiago Osório ; Demattê, José Alexandre Melo
Source: Soil Advances. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, CONDUTIVIDADE ELÉTRICA, DISTRIBUIÇÃO ESPACIAL, GAMAESPECTROMETRIA, MAPEAMENTO DO SOLO, SENSOR, SOLOS
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MELLO, Danilo César de et al. Integrating proximal geophysical sensing and machine learning for digital soil mapping: spatial prediction and model evaluation using a small dataset. Soil Advances, v. 3, p. 1-13, 2025Tradução . . Disponível em: https://doi.org/10.1016/j.soilad.2024.100024. Acesso em: 08 out. 2025.
APA
Mello, D. C. de, Veloso, G. V., Mello, M. F. de, Lana, M. G. de, Oliveira, I. de A., Mello, F. A. de O., et al. (2025). Integrating proximal geophysical sensing and machine learning for digital soil mapping: spatial prediction and model evaluation using a small dataset. Soil Advances, 3, 1-13. doi:10.1016/j.soilad.2024.100024
NLM
Mello DC de, Veloso GV, Mello MF de, Lana MG de, Oliveira I de A, Mello FA de O, Siqueira RG, Gomes LC, Fernandes-Filho EI, Schaefer CEGR, Francelino MR, Leite EP, Ferreira TO, Demattê JAM. Integrating proximal geophysical sensing and machine learning for digital soil mapping: spatial prediction and model evaluation using a small dataset [Internet]. Soil Advances. 2025 ; 3 1-13.[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/j.soilad.2024.100024
Vancouver
Mello DC de, Veloso GV, Mello MF de, Lana MG de, Oliveira I de A, Mello FA de O, Siqueira RG, Gomes LC, Fernandes-Filho EI, Schaefer CEGR, Francelino MR, Leite EP, Ferreira TO, Demattê JAM. Integrating proximal geophysical sensing and machine learning for digital soil mapping: spatial prediction and model evaluation using a small dataset [Internet]. Soil Advances. 2025 ; 3 1-13.[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/j.soilad.2024.100024
Artigo de periodico
Electronic tongue made of gelatin self-supporting films on printed electrodes to detect lactose (2024)
Piccinin, Ana Carolina Varanda; Soares, Andrey Coatrini ; Franco, Giuliana Thalina ; Bondancia, Thalita Jessika ; Soares, Juliana Coatrini ; Oliveira Junior, Osvaldo Novais de ; Mattoso, Luiz Henrique Capparelli
Source: Frontiers in Sensors. Unidade: IFSC
Subjects: SENSOR, MONITORAMENTO AMBIENTAL, APRENDIZADO COMPUTACIONAL, SENSORES BIOMÉDICOS, FILMES, PECUÁRIA LEITEIRA
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PICCININ, Ana Carolina Varanda et al. Electronic tongue made of gelatin self-supporting films on printed electrodes to detect lactose. Frontiers in Sensors, v. 5, 2024Tradução . . Disponível em: https://doi.org/10.3389/fsens.2024.1401077. Acesso em: 08 out. 2025.
APA
Piccinin, A. C. V., Soares, A. C., Franco, G. T., Bondancia, T. J., Soares, J. C., Oliveira Junior, O. N. de, & Mattoso, L. H. C. (2024). Electronic tongue made of gelatin self-supporting films on printed electrodes to detect lactose. Frontiers in Sensors, 5. doi:10.3389/fsens.2024.1401077
NLM
Piccinin ACV, Soares AC, Franco GT, Bondancia TJ, Soares JC, Oliveira Junior ON de, Mattoso LHC. Electronic tongue made of gelatin self-supporting films on printed electrodes to detect lactose [Internet]. Frontiers in Sensors. 2024 ; 5[citado 2025 out. 08 ] Available from: https://doi.org/10.3389/fsens.2024.1401077
Vancouver
Piccinin ACV, Soares AC, Franco GT, Bondancia TJ, Soares JC, Oliveira Junior ON de, Mattoso LHC. Electronic tongue made of gelatin self-supporting films on printed electrodes to detect lactose [Internet]. Frontiers in Sensors. 2024 ; 5[citado 2025 out. 08 ] Available from: https://doi.org/10.3389/fsens.2024.1401077
Artigo de periodico
Corrigendum to: "Intelligent methodologies: An integrated multi-modeling approach to predict adaptive mechanisms in farm animals" [Comput. Electron. Agric. 216 (2024) 108502] (2024)
Silveira, Robson Mateus Freitas ; Façanha, Débora Andréa Evangelista; McManus, Concepta; Bermejo Asensio, Luis Alberto; Silva, Iran José Oliveira da
Source: Computers and Electronics in Agriculture. Unidade: ESALQ
Subjects: ADAPTAÇÃO ANIMAL, ALGORITMOS, APRENDIZADO COMPUTACIONAL, INTELIGÊNCIA ARTIFICIAL, PRODUÇÃO ANIMAL, REGULAÇÃO DA TEMPERATURA CORPORAL ANIMAL, SENSOR
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SILVEIRA, Robson Mateus Freitas et al. Corrigendum to: "Intelligent methodologies: An integrated multi-modeling approach to predict adaptive mechanisms in farm animals" [Comput. Electron. Agric. 216 (2024) 108502]. Computers and Electronics in Agriculture, v. 218, p. 1, 2024Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2024.108673. Acesso em: 08 out. 2025.
APA
Silveira, R. M. F., Façanha, D. A. E., McManus, C., Bermejo Asensio, L. A., & Silva, I. J. O. da. (2024). Corrigendum to: "Intelligent methodologies: An integrated multi-modeling approach to predict adaptive mechanisms in farm animals" [Comput. Electron. Agric. 216 (2024) 108502]. Computers and Electronics in Agriculture, 218, 1. doi:10.1016/j.compag.2024.108673
NLM
Silveira RMF, Façanha DAE, McManus C, Bermejo Asensio LA, Silva IJO da. Corrigendum to: "Intelligent methodologies: An integrated multi-modeling approach to predict adaptive mechanisms in farm animals" [Comput. Electron. Agric. 216 (2024) 108502] [Internet]. Computers and Electronics in Agriculture. 2024 ; 218 1.[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/j.compag.2024.108673
Vancouver
Silveira RMF, Façanha DAE, McManus C, Bermejo Asensio LA, Silva IJO da. Corrigendum to: "Intelligent methodologies: An integrated multi-modeling approach to predict adaptive mechanisms in farm animals" [Comput. Electron. Agric. 216 (2024) 108502] [Internet]. Computers and Electronics in Agriculture. 2024 ; 218 1.[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/j.compag.2024.108673
Artigo de periodico
Analysis of macronutrients in soil using impedimetric multisensor arrays (2024)
Braunger, Maria Luisa ; Popolin Neto, Mário ; Kirsanov, Dmitry; Fier, Igor; Amaral, Lucas Rios do ; Shimizu, Flávio Makoto ; Corrêa, Daniel Souza ; Paulovich, Fernando Vieira ; Legin, Andrey; Oliveira Junior, Osvaldo Novais de ; Riul Junior, Antonio
Source: ACS Omega. Unidade: IFSC
Subjects: AGRICULTURA, FILMES FINOS, SENSOR, APRENDIZADO COMPUTACIONAL
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BRAUNGER, Maria Luisa et al. Analysis of macronutrients in soil using impedimetric multisensor arrays. ACS Omega, v. 9, n. 31, p. 33949-33958 + supporting information, 2024Tradução . . Disponível em: https://doi.org/10.1021/acsomega.4c04452. Acesso em: 08 out. 2025.
APA
Braunger, M. L., Popolin Neto, M., Kirsanov, D., Fier, I., Amaral, L. R. do, Shimizu, F. M., et al. (2024). Analysis of macronutrients in soil using impedimetric multisensor arrays. ACS Omega, 9( 31), 33949-33958 + supporting information. doi:10.1021/acsomega.4c04452
NLM
Braunger ML, Popolin Neto M, Kirsanov D, Fier I, Amaral LR do, Shimizu FM, Corrêa DS, Paulovich FV, Legin A, Oliveira Junior ON de, Riul Junior A. Analysis of macronutrients in soil using impedimetric multisensor arrays [Internet]. ACS Omega. 2024 ; 9( 31): 33949-33958 + supporting information.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acsomega.4c04452
Vancouver
Braunger ML, Popolin Neto M, Kirsanov D, Fier I, Amaral LR do, Shimizu FM, Corrêa DS, Paulovich FV, Legin A, Oliveira Junior ON de, Riul Junior A. Analysis of macronutrients in soil using impedimetric multisensor arrays [Internet]. ACS Omega. 2024 ; 9( 31): 33949-33958 + supporting information.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acsomega.4c04452
Artigo de periodico
Intelligent methodologies: An integrated multi-modeling approach to predict adaptive mechanisms in farm animals (2024)
Silveira, Robson Mateus Freitas ; Façanha, Débora Andréa Evangelista; McManus, Concepta; Bermejo Asensio, Luis Alberto; Silva, Iran José Oliveira da
Source: Computers and Electronics in Agriculture. Unidade: ESALQ
Subjects: ADAPTAÇÃO ANIMAL, ALGORITMOS, APRENDIZADO COMPUTACIONAL, INTELIGÊNCIA ARTIFICIAL, PRODUÇÃO ANIMAL, REGULAÇÃO DA TEMPERATURA CORPORAL ANIMAL, SENSOR
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ABNT
SILVEIRA, Robson Mateus Freitas et al. Intelligent methodologies: An integrated multi-modeling approach to predict adaptive mechanisms in farm animals. Computers and Electronics in Agriculture, v. 216, p. 1-11, 2024Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2023.108502. Acesso em: 08 out. 2025.
APA
Silveira, R. M. F., Façanha, D. A. E., McManus, C., Bermejo Asensio, L. A., & Silva, I. J. O. da. (2024). Intelligent methodologies: An integrated multi-modeling approach to predict adaptive mechanisms in farm animals. Computers and Electronics in Agriculture, 216, 1-11. doi:10.1016/j.compag.2023.108502
NLM
Silveira RMF, Façanha DAE, McManus C, Bermejo Asensio LA, Silva IJO da. Intelligent methodologies: An integrated multi-modeling approach to predict adaptive mechanisms in farm animals [Internet]. Computers and Electronics in Agriculture. 2024 ; 216 1-11.[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/j.compag.2023.108502
Vancouver
Silveira RMF, Façanha DAE, McManus C, Bermejo Asensio LA, Silva IJO da. Intelligent methodologies: An integrated multi-modeling approach to predict adaptive mechanisms in farm animals [Internet]. Computers and Electronics in Agriculture. 2024 ; 216 1-11.[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/j.compag.2023.108502
Artigo de periodico
Chemical weathering detection in the periglacial landscapes of Maritime Antarctica: new approach using geophysical sensors, topographic variables and machine learning algorithms (2023)
Mello, Danilo César de ; Veloso, Gustavo Vieira ; Moquedace, Cassio Marques; Oliveira, Isabelle de Angeli; Francelino, Márcio Rocha ; Oliveira, Fabio Soares de; Souza, José João Lelis Leal de ; Gomes, Lucas Carvalho; Schaefer, Carlos Ernesto Gonçalves Reynaud; Fernandes-Filho, Elpídio Inácio ; Medeiros-Júnior, Edgar ; Demattê, José Alexandre Melo
Source: Geoderma. Unidade: ESALQ
Subjects: ALGORITMOS, APRENDIZADO COMPUTACIONAL, ESPECTROFOTOMETRIA, GEOFÍSICA, INTEMPERISMO, LEVANTAMENTOS TOPOGRÁFICOS, PAISAGEM, SENSOR
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ABNT
MELLO, Danilo César de et al. Chemical weathering detection in the periglacial landscapes of Maritime Antarctica: new approach using geophysical sensors, topographic variables and machine learning algorithms. Geoderma, v. 438, p. 1-15, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.geoderma.2023.116615. Acesso em: 08 out. 2025.
APA
Mello, D. C. de, Veloso, G. V., Moquedace, C. M., Oliveira, I. de A., Francelino, M. R., Oliveira, F. S. de, et al. (2023). Chemical weathering detection in the periglacial landscapes of Maritime Antarctica: new approach using geophysical sensors, topographic variables and machine learning algorithms. Geoderma, 438, 1-15. doi:10.1016/j.geoderma.2023.116615
NLM
Mello DC de, Veloso GV, Moquedace CM, Oliveira I de A, Francelino MR, Oliveira FS de, Souza JJLL de, Gomes LC, Schaefer CEGR, Fernandes-Filho EI, Medeiros-Júnior E, Demattê JAM. Chemical weathering detection in the periglacial landscapes of Maritime Antarctica: new approach using geophysical sensors, topographic variables and machine learning algorithms [Internet]. Geoderma. 2023 ; 438 1-15.[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/j.geoderma.2023.116615
Vancouver
Mello DC de, Veloso GV, Moquedace CM, Oliveira I de A, Francelino MR, Oliveira FS de, Souza JJLL de, Gomes LC, Schaefer CEGR, Fernandes-Filho EI, Medeiros-Júnior E, Demattê JAM. Chemical weathering detection in the periglacial landscapes of Maritime Antarctica: new approach using geophysical sensors, topographic variables and machine learning algorithms [Internet]. Geoderma. 2023 ; 438 1-15.[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/j.geoderma.2023.116615
Artigo de periodico
Microfluidic E-tongue to diagnose bovine mastitis with milk samples using machine learning with decision tree models (2023)
Soares, Andrey Coatrini ; Soares, Juliana Coatrini ; Popolin Neto, Mário ; Mello, Suelen Scarpa de; Pinto, Danielle Dos Santos Cinelli; Carvalho, Wanessa Araújo; Gilmore, Michael Shaffer; Piazzetta, Maria Helena de Oliveira; Gobbi, Angelo Luiz ; Brandão, Humberto de Mello; Paulovich, Fernando Vieira ; Oliveira Junior, Osvaldo Novais de ; Mattoso, Luiz Henrique Capparelli
Source: Chemical Engineering Journal. Unidades: IFSC, ICMC
Subjects: MASTITE ANIMAL, ELETROQUÍMICA, APRENDIZADO COMPUTACIONAL, PECUÁRIA LEITEIRA, MONITORAMENTO AMBIENTAL, STAPHYLOCOCCUS, SENSOR
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ABNT
SOARES, Andrey Coatrini et al. Microfluidic E-tongue to diagnose bovine mastitis with milk samples using machine learning with decision tree models. Chemical Engineering Journal, v. 451, n. Ja 2023, p. 138523-1-138523-9, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.cej.2022.138523. Acesso em: 08 out. 2025.
APA
Soares, A. C., Soares, J. C., Popolin Neto, M., Mello, S. S. de, Pinto, D. D. S. C., Carvalho, W. A., et al. (2023). Microfluidic E-tongue to diagnose bovine mastitis with milk samples using machine learning with decision tree models. Chemical Engineering Journal, 451( Ja 2023), 138523-1-138523-9. doi:10.1016/j.cej.2022.138523
NLM
Soares AC, Soares JC, Popolin Neto M, Mello SS de, Pinto DDSC, Carvalho WA, Gilmore MS, Piazzetta MH de O, Gobbi AL, Brandão H de M, Paulovich FV, Oliveira Junior ON de, Mattoso LHC. Microfluidic E-tongue to diagnose bovine mastitis with milk samples using machine learning with decision tree models [Internet]. Chemical Engineering Journal. 2023 ; 451( Ja 2023): 138523-1-138523-9.[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/j.cej.2022.138523
Vancouver
Soares AC, Soares JC, Popolin Neto M, Mello SS de, Pinto DDSC, Carvalho WA, Gilmore MS, Piazzetta MH de O, Gobbi AL, Brandão H de M, Paulovich FV, Oliveira Junior ON de, Mattoso LHC. Microfluidic E-tongue to diagnose bovine mastitis with milk samples using machine learning with decision tree models [Internet]. Chemical Engineering Journal. 2023 ; 451( Ja 2023): 138523-1-138523-9.[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/j.cej.2022.138523
Parte de monografia/livro
A machine learning approach in wearable technologies (2023)
Ibáñez-Redín, Glenda Gisela ; Duarte, Oscar Salomón; Cagnani, Giovana Rosso ; Oliveira Junior, Osvaldo Novais de
Source: Machine learning for advanced functional materials. Unidades: IFSC, IQSC
Subjects: ELETROQUÍMICA, APRENDIZADO COMPUTACIONAL, SENSOR, INTELIGÊNCIA ARTIFICIAL
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IBÁÑEZ-REDÍN, Glenda Gisela et al. A machine learning approach in wearable technologies. Machine learning for advanced functional materials. Tradução . Singapore: Springer, 2023. . Disponível em: https://doi.org/10.1007/978-981-99-0393-1_3. Acesso em: 08 out. 2025.
APA
Ibáñez-Redín, G. G., Duarte, O. S., Cagnani, G. R., & Oliveira Junior, O. N. de. (2023). A machine learning approach in wearable technologies. In Machine learning for advanced functional materials. Singapore: Springer. doi:10.1007/978-981-99-0393-1_3
NLM
Ibáñez-Redín GG, Duarte OS, Cagnani GR, Oliveira Junior ON de. A machine learning approach in wearable technologies [Internet]. In: Machine learning for advanced functional materials. Singapore: Springer; 2023. [citado 2025 out. 08 ] Available from: https://doi.org/10.1007/978-981-99-0393-1_3
Vancouver
Ibáñez-Redín GG, Duarte OS, Cagnani GR, Oliveira Junior ON de. A machine learning approach in wearable technologies [Internet]. In: Machine learning for advanced functional materials. Singapore: Springer; 2023. [citado 2025 out. 08 ] Available from: https://doi.org/10.1007/978-981-99-0393-1_3
Artigo de periodico
Digital mapping of soil weathering using field geophysical sensor data coupled with covariates and machine learning (2023)
Mello, Danilo César de ; Ferreira, Tiago Osório ; Veloso, Gustavo Vieira ; Lana, Marcos Guedes de; Mello, Fellipe Alcantara de Oliveira ; Di Raimo, Luis Augusto Di Loreto ; Cabrero, Diego Ribeiro Oquendo; Souza, José João Lelis Leal de ; Fernandes-Filho, Elpídio Inácio ; Francelino, Márcio Rocha ; Demattê, José Alexandre Melo
Source: Journal of South American Earth Sciences. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, ESPECTROMETRIA, IMAGEAMENTO DE SATÉLITE, INTEMPERISMO, MAPEAMENTO DO SOLO, MORFOLOGIA DO SOLO, SENSOR
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ABNT
MELLO, Danilo César de et al. Digital mapping of soil weathering using field geophysical sensor data coupled with covariates and machine learning. Journal of South American Earth Sciences, v. 128, p. 1-15, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.jsames.2023.104449. Acesso em: 08 out. 2025.
APA
Mello, D. C. de, Ferreira, T. O., Veloso, G. V., Lana, M. G. de, Mello, F. A. de O., Di Raimo, L. A. D. L., et al. (2023). Digital mapping of soil weathering using field geophysical sensor data coupled with covariates and machine learning. Journal of South American Earth Sciences, 128, 1-15. doi:10.1016/j.jsames.2023.104449
NLM
Mello DC de, Ferreira TO, Veloso GV, Lana MG de, Mello FA de O, Di Raimo LADL, Cabrero DRO, Souza JJLL de, Fernandes-Filho EI, Francelino MR, Demattê JAM. Digital mapping of soil weathering using field geophysical sensor data coupled with covariates and machine learning [Internet]. Journal of South American Earth Sciences. 2023 ; 128 1-15.[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/j.jsames.2023.104449
Vancouver
Mello DC de, Ferreira TO, Veloso GV, Lana MG de, Mello FA de O, Di Raimo LADL, Cabrero DRO, Souza JJLL de, Fernandes-Filho EI, Francelino MR, Demattê JAM. Digital mapping of soil weathering using field geophysical sensor data coupled with covariates and machine learning [Internet]. Journal of South American Earth Sciences. 2023 ; 128 1-15.[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/j.jsames.2023.104449
Artigo de periodico
A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes (2022)
Mello, Danilo César de ; Veloso, Gustavo Vieira ; Lana, Marcos Guedes de; Mello, Fellipe Alcantara de Oliveira ; Poppiel, Raul Roberto ; Cabrero, Diego Ribeiro Oquendo; Di Raimo, Luis Augusto Di Loreto ; Schaefer, Carlos Ernesto Gonçalves Reynaud; Fernandes Filho, Elpídio Inácio ; Leite, Emilson Pereira; Demattê, José Alexandre Melo
Source: Geoscientific Model Development. Unidade: ESALQ
Subjects: ALGORITMOS, APRENDIZADO COMPUTACIONAL, FRAMEWORKS, GEOFÍSICA, MODELAGEM DE DADOS, SENSOR, SOLOS
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ABNT
MELLO, Danilo César de et al. A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes. Geoscientific Model Development, v. 15, p. 1219–1246, 2022Tradução . . Disponível em: https://doi.org/10.5194/gmd-15-1219-2022. Acesso em: 08 out. 2025.
APA
Mello, D. C. de, Veloso, G. V., Lana, M. G. de, Mello, F. A. de O., Poppiel, R. R., Cabrero, D. R. O., et al. (2022). A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes. Geoscientific Model Development, 15, 1219–1246. doi:10.5194/gmd-15-1219-2022
NLM
Mello DC de, Veloso GV, Lana MG de, Mello FA de O, Poppiel RR, Cabrero DRO, Di Raimo LADL, Schaefer CEGR, Fernandes Filho EI, Leite EP, Demattê JAM. A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes [Internet]. Geoscientific Model Development. 2022 ; 15 1219–1246.[citado 2025 out. 08 ] Available from: https://doi.org/10.5194/gmd-15-1219-2022
Vancouver
Mello DC de, Veloso GV, Lana MG de, Mello FA de O, Poppiel RR, Cabrero DRO, Di Raimo LADL, Schaefer CEGR, Fernandes Filho EI, Leite EP, Demattê JAM. A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes [Internet]. Geoscientific Model Development. 2022 ; 15 1219–1246.[citado 2025 out. 08 ] Available from: https://doi.org/10.5194/gmd-15-1219-2022
Artigo de periodico
Pedogenetic processes operating at different intensities inferred by geophysical sensors and machine learning algorithms (2022)
Mello, Danilo Cesar de ; Ferreira, Tiago Osorio ; Veloso, Gustavo Vieira ; Lana, Marcos Guedes de; Mello, Fellipe Alcantara de Oliveira ; Di Raimo, Luis Augusto Di Loreto ; Schaefer, Carlos Ernesto Gonçalves Reynaud; Francelino, Márcio Rocha ; Fernandes-Filho, Elpídio Inácio ; Dematte, Jose Alexandre Melo
Source: Catena. Unidade: ESALQ
Subjects: ALGORITMOS, APRENDIZADO COMPUTACIONAL, SENSOR, SOLOS
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ABNT
MELLO, Danilo Cesar de et al. Pedogenetic processes operating at different intensities inferred by geophysical sensors and machine learning algorithms. Catena, v. 216, p. 1-15, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.catena.2022.106370. Acesso em: 08 out. 2025.
APA
Mello, D. C. de, Ferreira, T. O., Veloso, G. V., Lana, M. G. de, Mello, F. A. de O., Di Raimo, L. A. D. L., et al. (2022). Pedogenetic processes operating at different intensities inferred by geophysical sensors and machine learning algorithms. Catena, 216, 1-15. doi:10.1016/j.catena.2022.106370
NLM
Mello DC de, Ferreira TO, Veloso GV, Lana MG de, Mello FA de O, Di Raimo LADL, Schaefer CEGR, Francelino MR, Fernandes-Filho EI, Dematte JAM. Pedogenetic processes operating at different intensities inferred by geophysical sensors and machine learning algorithms [Internet]. Catena. 2022 ; 216 1-15.[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/j.catena.2022.106370
Vancouver
Mello DC de, Ferreira TO, Veloso GV, Lana MG de, Mello FA de O, Di Raimo LADL, Schaefer CEGR, Francelino MR, Fernandes-Filho EI, Dematte JAM. Pedogenetic processes operating at different intensities inferred by geophysical sensors and machine learning algorithms [Internet]. Catena. 2022 ; 216 1-15.[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/j.catena.2022.106370
Trabalho de evento-resumo
Machine learning applied for designing conductive polymers based electrochemical sensors for pesticide (2022)
Galdino, Nathália Magno; Baum, Fábio; Köche, Ariadne; Manica, Luiza; Santos, Jacqueline Ferreira Leite; Oliveira Junior, Osvaldo Novais de
Source: Program. Conference titles: Brazil MRS Meeting. Unidade: IFSC
Subjects: APRENDIZADO COMPUTACIONAL, DESCOBERTA DE CONHECIMENTO, SENSOR
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ABNT
GALDINO, Nathália Magno et al. Machine learning applied for designing conductive polymers based electrochemical sensors for pesticide. 2022, Anais.. Rio de Janeiro: Sociedade Brasileira de Pesquisa em Materiais - SBPMat, 2022. Disponível em: https://repositorio.usp.br/directbitstream/d9bc3130-ff07-4699-8029-34352397618d/3098363.pdf. Acesso em: 08 out. 2025.
APA
Galdino, N. M., Baum, F., Köche, A., Manica, L., Santos, J. F. L., & Oliveira Junior, O. N. de. (2022). Machine learning applied for designing conductive polymers based electrochemical sensors for pesticide. In Program. Rio de Janeiro: Sociedade Brasileira de Pesquisa em Materiais - SBPMat. Recuperado de https://repositorio.usp.br/directbitstream/d9bc3130-ff07-4699-8029-34352397618d/3098363.pdf
NLM
Galdino NM, Baum F, Köche A, Manica L, Santos JFL, Oliveira Junior ON de. Machine learning applied for designing conductive polymers based electrochemical sensors for pesticide [Internet]. Program. 2022 ;[citado 2025 out. 08 ] Available from: https://repositorio.usp.br/directbitstream/d9bc3130-ff07-4699-8029-34352397618d/3098363.pdf
Vancouver
Galdino NM, Baum F, Köche A, Manica L, Santos JFL, Oliveira Junior ON de. Machine learning applied for designing conductive polymers based electrochemical sensors for pesticide [Internet]. Program. 2022 ;[citado 2025 out. 08 ] Available from: https://repositorio.usp.br/directbitstream/d9bc3130-ff07-4699-8029-34352397618d/3098363.pdf
Artigo de periodico
Detection of a SARS-CoV-2 sequence with genosensors using data analysis based on information visualization and machine learning techniques (2021)
Soares, Juliana Coatrini ; Soares, Andrey Coatrini ; Rodrigues, Valquiria da Cruz; Oiticica, Pedro Ramon Almeida ; Raymundo-Pereira, Paulo Augusto ; Bott Neto, José Luiz ; Buscaglia, Lorenzo A ; Castro, Lucas Daniel Chiba de ; Ribas, Lucas Correia ; Scabini, Leonardo Felipe dos Santos ; Brazaca, Lais Canniatti ; Correa, Daniel S. ; Mattoso, Luiz Henrique C; Oliveira, Maria Cristina Ferreira de ; Carvalho, André Carlos Ponce de Leon Ferreira de ; Carrilho, Emanuel ; Bruno, Odemir Martinez ; Melendez, Matias Eliseo ; Oliveira Junior, Osvaldo Novais de
Source: Materials Chemistry Frontiers. Unidades: ICMC, IQSC, IFSC
Subjects: CORONAVIRUS, COVID-19, DIAGNÓSTICO, SENSOR, SEQUÊNCIA DO DNA, VISUALIZAÇÃO, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SOARES, Juliana Coatrini et al. Detection of a SARS-CoV-2 sequence with genosensors using data analysis based on information visualization and machine learning techniques. Materials Chemistry Frontiers, v. 5, n. 15, p. 5658-5670, 2021Tradução . . Disponível em: https://doi.org/10.1039/D1QM00665G. Acesso em: 08 out. 2025.
APA
Soares, J. C., Soares, A. C., Rodrigues, V. da C., Oiticica, P. R. A., Raymundo-Pereira, P. A., Bott Neto, J. L., et al. (2021). Detection of a SARS-CoV-2 sequence with genosensors using data analysis based on information visualization and machine learning techniques. Materials Chemistry Frontiers, 5( 15), 5658-5670. doi:10.1039/D1QM00665G
NLM
Soares JC, Soares AC, Rodrigues V da C, Oiticica PRA, Raymundo-Pereira PA, Bott Neto JL, Buscaglia LA, Castro LDC de, Ribas LC, Scabini LF dos S, Brazaca LC, Correa DS, Mattoso LHC, Oliveira MCF de, Carvalho ACP de LF de, Carrilho E, Bruno OM, Melendez ME, Oliveira Junior ON de. Detection of a SARS-CoV-2 sequence with genosensors using data analysis based on information visualization and machine learning techniques [Internet]. Materials Chemistry Frontiers. 2021 ; 5( 15): 5658-5670.[citado 2025 out. 08 ] Available from: https://doi.org/10.1039/D1QM00665G
Vancouver
Soares JC, Soares AC, Rodrigues V da C, Oiticica PRA, Raymundo-Pereira PA, Bott Neto JL, Buscaglia LA, Castro LDC de, Ribas LC, Scabini LF dos S, Brazaca LC, Correa DS, Mattoso LHC, Oliveira MCF de, Carvalho ACP de LF de, Carrilho E, Bruno OM, Melendez ME, Oliveira Junior ON de. Detection of a SARS-CoV-2 sequence with genosensors using data analysis based on information visualization and machine learning techniques [Internet]. Materials Chemistry Frontiers. 2021 ; 5( 15): 5658-5670.[citado 2025 out. 08 ] Available from: https://doi.org/10.1039/D1QM00665G
Artigo de periodico
Machine learning used to create a multidimensional calibration space for sensing and biosensing data (2021)
Popolin Neto, Mário ; Soares, Andrey Coatrini ; Oliveira Junior, Osvaldo Novais de ; Paulovich, Fernando Vieira
Source: Bulletin of the Chemical Society of Japan. Unidades: IFSC, ICMC
Subjects: BIOTECNOLOGIA, APRENDIZADO COMPUTACIONAL, SENSOR, FILMES FINOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
POPOLIN NETO, Mário et al. Machine learning used to create a multidimensional calibration space for sensing and biosensing data. Bulletin of the Chemical Society of Japan, v. 94, n. 5, p. 1553-1562, 2021Tradução . . Disponível em: https://doi.org/10.1246/bcsj.20200359. Acesso em: 08 out. 2025.
APA
Popolin Neto, M., Soares, A. C., Oliveira Junior, O. N. de, & Paulovich, F. V. (2021). Machine learning used to create a multidimensional calibration space for sensing and biosensing data. Bulletin of the Chemical Society of Japan, 94( 5), 1553-1562. doi:10.1246/bcsj.20200359
NLM
Popolin Neto M, Soares AC, Oliveira Junior ON de, Paulovich FV. Machine learning used to create a multidimensional calibration space for sensing and biosensing data [Internet]. Bulletin of the Chemical Society of Japan. 2021 ; 94( 5): 1553-1562.[citado 2025 out. 08 ] Available from: https://doi.org/10.1246/bcsj.20200359
Vancouver
Popolin Neto M, Soares AC, Oliveira Junior ON de, Paulovich FV. Machine learning used to create a multidimensional calibration space for sensing and biosensing data [Internet]. Bulletin of the Chemical Society of Japan. 2021 ; 94( 5): 1553-1562.[citado 2025 out. 08 ] Available from: https://doi.org/10.1246/bcsj.20200359
Artigo de periodico
Sensing and biosensing in the world of autonomous machines and intelligent systems (2021)
Oliveira Junior, Osvaldo Novais de ; Oliveira, Maria Cristina Ferreira de
Source: Frontiers in Sensors. Unidades: IFSC, ICMC
Subjects: NANOTECNOLOGIA, APRENDIZADO COMPUTACIONAL, INTELIGÊNCIA ARTIFICIAL, FUTURO, COMPUTAÇÃO MÓVEL, SENSOR
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
OLIVEIRA JUNIOR, Osvaldo Novais de e OLIVEIRA, Maria Cristina Ferreira de. Sensing and biosensing in the world of autonomous machines and intelligent systems. Frontiers in Sensors, v. 2, p. 752754-1-752754-7, 2021Tradução . . Disponível em: https://doi.org/10.3389/fsens.2021.752754. Acesso em: 08 out. 2025.
APA
Oliveira Junior, O. N. de, & Oliveira, M. C. F. de. (2021). Sensing and biosensing in the world of autonomous machines and intelligent systems. Frontiers in Sensors, 2, 752754-1-752754-7. doi:10.3389/fsens.2021.752754
NLM
Oliveira Junior ON de, Oliveira MCF de. Sensing and biosensing in the world of autonomous machines and intelligent systems [Internet]. Frontiers in Sensors. 2021 ; 2 752754-1-752754-7.[citado 2025 out. 08 ] Available from: https://doi.org/10.3389/fsens.2021.752754
Vancouver
Oliveira Junior ON de, Oliveira MCF de. Sensing and biosensing in the world of autonomous machines and intelligent systems [Internet]. Frontiers in Sensors. 2021 ; 2 752754-1-752754-7.[citado 2025 out. 08 ] Available from: https://doi.org/10.3389/fsens.2021.752754

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